Title: Data mining with a parallel rule induction system based on gene expression programming

Authors: Wagner Rodrigo Weinert, Heitor Silverio Lopes

Addresses: Federal Institute of Education, Science and Techonology of Parana, R. Antonio Carlos Rodrigues 453, 83215-750 Paranagua (PR), Brazil. ' Federal University of Technology – Parana, Av. Sete de Setembro 3165, 80230-901 Curitiba (PR), Brazil

Abstract: A parallel rule induction system based on gene expression programming (GEP) is reported in this paper. The system was developed for data classification. The parallel processing environment was implemented on a cluster using a message-passing interface. A master-slave GEP was implemented according to the Michigan approach for representing a solution for a classification problem. A multiple master-slave system (islands) was implemented in order to observe the co-evolution effect. Experiments were done with ten datasets, and algorithms were systematically compared with C4.5. Results were analysed from the point of view of a multi-objective problem, taking into account both predictive accuracy and comprehensibility of induced rules. Overall results indicate that the proposed system achieves better predictive accuracy with shorter rules, when compared with C4.5.

Keywords: evolutionary computation; gene expression programming; GEP; data mining; parallel rule induction; data classification; bioinformatics.

DOI: 10.1504/IJICA.2011.041914

International Journal of Innovative Computing and Applications, 2011 Vol.3 No.3, pp.136 - 143

Received: 30 Aug 2010
Accepted: 29 Jan 2011
Published online: 21 Mar 2015 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article